research article circulating microrna profiles differ...

Research ArticleCirculating MicroRNA Profiles Differ between Qi-Stagnationand Qi-Deficiency in Coronary Heart Disease Patients withBlood Stasis Syndrome

Jincai Hou,1,2 Jun Wang,3 Chengren Lin,1,2 Jianhua Fu,1,2 Jianxun Ren,1,2 Lei Li,1,2

Hao Guo,1,2 Xiao Han,1,2 and Jianxun Liu1,2

1Xiyuan Hospital of China Academy of Chinese Medical Sciences, 1 Xiyuan Caochang, Haidian District, Beijing 100091, China2Beijing Key Laboratory of Pharmacology of Chinese Materia Medica, Institute of Basic Medical Sciences of Xiyuan Hospital,1 Xiyuan Caochang, Haidian District, Beijing 100091, China3Institute of Basic Theory, China Academy of Chinese Medical Sciences, 16 Dong Zhi Men Nei Nan Xiao Jie, Beijing 100700, China

Correspondence should be addressed to Jianxun Liu; [email protected]

Received 14 August 2014; Accepted 6 November 2014; Published 4 December 2014

Academic Editor: Karl Wah-Keung Tsim

Copyright © 2014 Jincai Hou et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

We compared the circulating microRNA profiles of Qi-stagnation (QSB) and Qi-deficiency (QDB) in coronary heart disease(CHD) patients with blood stasis syndrome. Twenty-nine CHD patients were divided into QSB group and QDB group. Theanalysis was carried out through comparing their circulating microRNA profiles and the following bioinformatics analysis. Thenumber of differential miRNAs in QDB group was much more than that in QSB group. Functional annotations of the differentiallyexpressed miRNAs target genes in the QSB group and QDB group were, respectively, related to regulation of cellular componentorganization, regulation of glucosemetabolic process, and so forth and protein kinase cascade, phosphatemetabolic process, and soforth. KEGG pathway analysis showed that the process Qi-deficiency was associated with phagocytosis including endocytosis andmTOR signaling pathway. Specifically, pathway of cell adhesion molecules played the crucial role in the pathological process of Qi-stagnation, with a unique upregulation except for pathways associated with cancer signal. MicroRNA-gene-net analysis indicatedthat let-7c, miR-4487, miR-619, miR-8075, miR-6735, and miR-32-5p and miR-17-5p, miR-130a, and miR 320 family had the mostimportant and extensive regulatory function for Qi-stagnation syndromes andQi-deficiency syndromes, respectively. Differentiallyexpressed miRNAs and concerned pathways suggest different molecular mechanisms that may mediate the pathological process ofQSB and QDB syndromes.

1. Introduction

Coronary heart disease (CHD), characterized by myocardialischemia or necrosis caused by vascular stenosis or occlusion,is one of the leading causes of deaths and hospital admis-sions worldwide and constitutes the leading cause of diseaseburden in the world according to the 2010 Global Burdenof Disease Study (GBD) [1, 2]. Traditional Chinese medicine(TCM), with a 3000-year-old history that includes uniquetheories for aetiology and systems of diagnosis and treatmenthas been proven to be an effective classification method inpatient stratification integrated with biomedical diagnosticmethod [3, 4]. TCM patterns have been used in China for

thousands of years and are still playing an important rolein the treatment of chronic diseases such as CHD. In TCM,the diagnosis, clinical evaluation, and treatment of CHD arebased on signs and subjective symptoms according to theunique concept of “wholism.” CHD treatments are based onTCMdiagnostics and syndrome differentiation which are thecomprehensive responses of a certain stage in the diseaseprocess [5].

Chinese medicine holds that blood stasis syndrome isa common reason responsible for CHD in clinic of Chi-nese medicine due to Qi-stagnation (QSB) or Qi-deficiency(QDB). At present, many researchers consider that objectivesigns of blood stasis are reflected in microcirculation related

Hindawi Publishing CorporationEvidence-Based Complementary and Alternative MedicineVolume 2014, Article ID 926962, 9 pageshttp://dx.doi.org/10.1155/2014/926962

2 Evidence-Based Complementary and Alternative Medicine

to vessel and cell function, such as vascular diastolic dysfunc-tion, abnormal platelet function, blood viscosity, and bloodcell adhesion. As for QSB and QDB, there exists differentbiological basis and objective signs, with their respectiveunique characteristics of pathological changes.

MicroRNAs (miRNAs) are endogenous small RNAmolecules best known for their function in posttranscrip-tional gene regulation. More than 60% of protein-codinggenes may be targeted by miRNAs [6], mainly throughtranslational repression and degradation of target mRNAs.MiRNAs are pivotal modulators of mammalian cardiovascu-lar development and disease and can be steadily found in thesystemic circulation of both animals and humans, where theyshow a remarkable stability probably due to internalizationin vesicles and binding to circulating proteins and othermolecules [7]. Since their levels may significantly changeupon stress, circulating miRNAs have been proposed asdiagnostic biomarkers in different pathologic conditions [8].

The characterization and differentiation of QSB andQDBsyndromes have played an important role in the clinicalpractice of TCM for CHD. We speculated that miRNAs ofperipheral blood have been a major parameter in discrimi-nating the QSB andQDB syndromes and affected the appear-ance of the CHD patients with QSB and QDB syndromes.Therefore, we investigated the possible relationship betweenthe syndromes as described in TCM practice and miRNAsprofiles to bridge the gap between traditional syndromediagnosis and molecular systems biology.

2. Materials and Methods

2.1. Participant Recruitment. This study was approved bythe Medical Ethical Committee Xiyuan Hospital of ChinaAcademy of Chinese Medical Sciences (2011XL008-2). All 19healthy volunteers reported no CHD and exhibited a normalsyndrome as judged by TCM doctors. Delayed, writteninformed consent was obtained from all enrollees after theywere clinically stabilized. 29 patients presenting with CHDwere identified in Xiyuan Hospital of China Academy ofChinese Medical Sciences during the time between March2012 and June 2013. Of the 29 patients screened for inclusion,7 fulfilled study inclusion criteria for QSB group and 22fulfilled study inclusion criteria for QDB group.

2.2. Sample Acquisition andHandling. 10milliliters of venousblood was obtained from ulnar vein and transferred in asterile fashion into vacuum blood collection tubes. Sampleswere placed immediately on ice and then centrifuged at 4∘Cfor 20min at 3000 rpm to obtain the serum which was takento the study laboratory within 10 minutes.

2.3. RNA Isolation and RNA Amplification. Total RNAincluding small RNA and miRNAs was isolated from serumsamples by TRIzol reagent (Invitrogen, Canada) and purifiedusing RNeasyMini Kit (Qiagen, German), including a DNasedigestion treatment. We ensure that the purification methodretains slow molecular weight (LMW) RNA. RNA concen-trations were determined by the absorbance at 260 nm and

quality control standards were 𝐴260/𝐴280= 1.8–2.1, using

NanoDrop 2000 (Thermo, America).

2.4. Affymetrix miRNA Microarray. RNA was labeled usingFlashTag Biotin HSR labeling kit as the manufacturer’sinstructions (Genisphere, America). Labeled RNA washybridized to GeneChip microRNA 3.0 array (Affymetrix,America) according to the user manuals. Affymetrix Expres-sion Console Software (version 1.3.1) was used for microarrayanalysis, including data normalization, summarization, andquality control assessment. Median-centric normalizationwas used for the custom microRNA oligonucleotide chips.Affymetrix chips were normalized using the robust multichipanalysis (RMA) procedure. Differentially expressed miRNAswere identified based on RVM 𝑡-test analysis. Differentiallyexpressed miRNAs with at least 1.5-fold change in eitherdirection with 𝑃 < 0.05 were considered to be up- ordownregulated.

2.5. Quantitative Real Time RT-PCR Validation. The cDNAwas subjected to real time quantitative PCR with definedprimers and Power SYBR Green PCR Master Mix (AppliedBiosystems, Foster City, CA, USA). The data were analyzedusing the ABI 7000 system SDS software (Applied Biosys-tems, Foster City, CA, USA). All the experiments wereperformed in duplicate and relative expression levels of thesemicroRNAs were determined by the 2−ΔΔCt method.

2.6. Gene Ontology (GO) Analysis. Gene ontology analysishas been carried out using DAVID and GSEA. ID of differ-ential expressed genes was uploaded to the DAVID databaseand the analysis was performed based on their respectivemolecular function, biological process, or cellular compo-nent. Functional categories were enriched within genes thatwere differentially expressed between the QSB and QDBgroups. Top ten GO functional categories that genes mainlyinvolved in were selected for specific analysis.

2.7. Pathway Analysis. Normalised signal intensities fromeach experimental condition for the differentially expressedgeneswere uploaded to theKEGGdatabasewhich is to collectpathway maps that computerize the network information ofmolecular interaction. KEGG analysis (KEGG data version:Release 70.1, June 1, 2014) is used for discovering the relationthat is not easily visible from the changes of individual genes.Pathways that had significant changes of 𝑃 < 0.05 and foldchange > 1.5 were identified for further analysis.

2.8. MicroRNA-Gene-Net Analysis. To build a miRNA-gene-network, the relationship between miRNAs and genes wascounted by their differential expression values and accord-ing to their interactions in the Sanger (Targetscan&miRa-nda) miRNA database. TargetScan 6.2 (http://www.targets-can.org/) in conjunction with the miRanda version August2010 Release (available at: http://www.microrna.org/) wasused to predict the targets of the miRNAs. The circlesrepresented target genes and the squares represented miR-NAs. The relationships between miRNAs and target genes

Evidence-Based Complementary and Alternative Medicine 3

Table 1: Demographic characteristics of CHD patients with QSB and QDB syndrome.

QSB (𝑁 = 7) QDB (𝑁 = 22)Demographics:

Mean age (years) 65.43 ± 9.5 66.55 ± 11.7Sex (Male) 4 8Heart rate (BPM) 83.2 ± 6.8 77.8 ± 10.7Blood pressure (SBP/DBP) 144 ± 12.1/90.6 ± 11.3 140.5 ± 5.23/81.8 ± 4.1Body mass index (kg/m2), mean ± SE 27.9 ± 0.6 26.2 ± 0.85

Comorbid conditions:Hypertension 3 (42) 15 (68)Diabetes mellitus 1 (14) 4 (18)Hyperlipidemia 1 (14) 4 (18)Current smoking 4 (57) 13 (59)Alcohol 2 (28) 6 (27)

Prehospital medications:Statins 5 (71) 20 (90)Beta blocker 1 (14) 5 (22)Angiotensin-converting enzyme inhibitor/angiotensin receptor blocker 2 (28) 8 (36)Calcium channel blocker 2 (28) 6 (27)Aspirin 3 (43) 9 (41)Nitroglycerin 3 (43) 7 (32)

were represented by edges. The center of the network wasrepresented by degree which is the contribution of onemiRNA to the genes around or the contribution of one geneto the miRNAs around [9]. The key miRNA and gene in thenetwork always have the biggest degrees.

3. Results

3.1. Patient Demographic. As shown in Table 1, the age ofQSBgroup (65.43 ± 9.55) had no significant difference comparedwith QDB (66.55 ± 11.74). Most participants presenting withQSB andQDB syndromes in our cohort were uniformlymale,with the percentage of 71.43% in QSB group and 63.64%in QDB group. 57.1% of QSB patients and 72.7% of QDBpatients had complications including hypertension, diabetes,and hyperlipidemia.Themajority of participants in bothQSBand QDB groups had a history of hypertension (60% and93.7%, resp.) and a minority had a history of diabetes orhyperlipidemia (40% and 16.3%, resp.).

3.2. Identification of miRNAs from QSB and QDB Syndromesby miRNA Array. MiRNAs that are differentially expressedbetween QSB and QDB groups which are expected to con-tribute to functional differences among the CHD patients.Therefore, we identified miRNAs that are differentiallyexpressed (𝑃 < 0.05) between QSB and QDB by microar-rays. Compared with the healthy patients, we detected 21differentially expressedmiRNAs, with 16 (76.2%) upregulatedmiRNAs and 5 (23.8%) downregulated miRNAs. 33 differ-entially expressed miRNAs were found in QDB group, with21 (63.7%) upregulated miRNAs and 12 (36.3%) downregu-lated miRNAs. Figure 1 displayed the differentially expressedmiRNAs overlapping and nonoverlapping in QSB and QDB

groups, which showed that 5 upregulated (Figure 1(a))and 4 downregulated (Figure 1(b)) differentially expressedmiRNAs overlapping among the 2 groups.

3.3. qRT-PCR Validation. To determine that the microarray-basedmiRNAs detections were reliable for the determinationof miRNAs expression patterns in the serum, we performedSYBR green-based real time quantitative PCR on upregulatedor downregulated miRNAs in the QSB and QDB groupsfrom microarray data (FDR 𝑃 < 0.05; fold change > 1.5).All randomly selected miRNAs were in agreement with themicroarray analyses in terms of the direction of the observeddifferential expression (Figure 1(c)). Specifically, the observedlog2fold changes with qRT-PCR of the upregulated miRNAs

(miR-451a, miR-23b, miR-455, and miR-32) were the samepositive tendency as obtained with the microarray data.Equally, the observed log

2fold changes with qRT-PCR of the

down-regulated miRNAs (miR-320c and miR-619) were thesame negative tendency as obtainedwith themicroarray data.

3.4. Gene Ontology (GO) Analysis of the miRNAs TargetGenes. In an effort to better understand molecular func-tion or biological process difference between circulatingmiRNA profiles in patients with QSB and QDB syndromes,we compared the gene ontology of miRNAs target genes.Venn diagram displayed the overlapping and nonoverlappingfunctional annotations in QSB and QDB groups, whichshowed that 123 upregulated (Figure 2(a)) and 11 down-regulated (Figure 2(b)) differentially functional annotationsoverlapped among the QSB and QDB groups. According tothe GO analysis, the obvious upregulated and downregu-lated functional annotations of the differentially expressedmiRNAs target genes in the QSB group (Figure 2(c)) were


QSB QDB

11 5

Upregulated

16

(a)

QSB QDB

1

Downregulated

84

(b)

0 2 4

Q-PCRmicroRNA microarray

−4 −2

Log2

fold change

(c)

Figure 1: (a) Section of upregulated differentially miRNAs overlapping and nonoverlapping in QSB and QDB groups. (b) Section ofdownregulated differentially miRNAs overlapping and nonoverlapping in QSB and QDB groups. (c) Real time qRT-PCR validation of themicroarray results: microarray results compared to qRT-PCR results relative to the OC.The obvious upregulated or downregulated miRNAsin QSB and QDB groups from microarray data (FDR 𝑃 < 0.05; fold change > 1.5) were randomly selected. Expression changes are depictedas log

2

fold change (𝑥-axis). miRNAs symbols are shown on the other side of the column.

related to regulation of cellular component organization,regulation of glucose metabolic process, cell-cell signaling,and so forth and protein import into nucleus, nuclear import,nucleocytoplasmic transport, and so forth, respectively.Based on the function of differentially expressed miRNAstarget genes in the QDB group (Figure 2(d)), the obviousupregulated and downregulated functional annotations weremainly related to protein kinase cascade, regulation of smallGTPase mediated signal transduction, phosphate metabolicprocess, and so forth and vesicle-mediated transport, nega-tive regulation of macromolecule metabolic process, enzymelinked receptor protein signaling pathway, and so forth,respectively.

3.5. Pathway Analysis of miRNAs Target Genes. Basedon the KEGG database, 4 upregulated (Figure 3(a)) and3 downregulated (Figure 3(b)) overlapping pathways wereidentified in QSB group and QDB group including pathwaysin cancer, TGF-beta signaling pathway, and calcium signaling

pathway. Moreover, 6 pathways were remarkably upregulatedin the QSB group, and 8 and 25 pathways were significantlydownregulated in QDB group. Table 2 demonstrated that celladhesion molecules (CAMs) played the crucial role in thepathological process of Qi-stagnation, with a significant andunique upregulation besides the rest cancer related pathways.Table 3 displayed that Qi-deficiency was associated withendocytosis and mTOR signaling pathway which is involvedin the process of autophagy.

3.6. MicroRNA-Gene-Net Analysis. Gene regulatory net-works composed of miRNAs and their target mRNAs areexpected to be important for the biological function of thespecific syndromes. Therefore, we carried out pathway anal-ysis for miRNAs that were differentially expressed betweenQSB and QDB and that belonged to miRNA clusters orfamilies.The network diagram of QSB group showed the coremiRNAs and their target genes including let-7c, miR-4487,miR-619, miR-8075, miR-6735, and miR-32-5p (Figure 3(c)).


QSB QDB

71

Upregulated

172123

(a)

QSB QDB

27

Downregulated

24511

(b)

−4.00E−02

−3.00E−02

−2.00E−02

−1.00E−02

0.00E+00

1.00E−02

Macromolecule catabolic processCellular macromolecule catabolic processNLS-bearing substrate import into nucleusProtein importProtein localization in organelleProtein localization in nucleusNuclear transportNucleocytoplasmic transportNuclear importProtein import into nucleus

Regulation of cell migrationCell projection assembly

GastrulationGastrulation with mouth forming second

Regulation of cell motionRegulation of cellular carbohydrate metabolic process

Regulation of carbohydrate metabolic processCell-cell signaling

Regulation of glucose metabolic processRegulation of cellular component organization

P value

(c)

−2.00E−04

−1.00E−04

0.00E+00

1.00E−04

2.00E−04

3.00E−04

4.00E−04

5.00E−04

6.00E−04

Regulation of ARF protein signal transductionTransmembrane receptor protein tyrosine kinase signaling pathwayRegulation of system processSmall GTPase mediated signal transductionRegulation of small GTPase mediated signal transductionNeuron developmentNeuron differentiationEnzyme linked receptor protein signaling pathwayNegative regulation of macromolecule metabolic processVesicle-mediated transport

Intracellular receptor-mediated signaling pathwayPositive regulation of developmental process

Blood vessel morphogenesisSynaptic vesicle transport

Intracellular transportNeuron migration

Phosphorus metabolic processPhosphate metabolic process

Regulation of small GTPase mediated signal

Protein kinase cascade

P value

transduction

(d)

Figure 2: (a) Section of overlapping and nonoverlapping upregulated functional annotations from the differentially expressedmiRNAs targetgenes. (b) Section of overlapping and nonoverlapping downregulated functional annotations from the differentially expressedmiRNAs targetgenes. (c) Bar graphs showed the most obvious upregulated and downregulated functional annotations of QSB group. (d) Bar graphs showedthe most obvious upregulated and downregulated functional annotations of QDB group. Expression changes are depicted as 𝑃 value (𝑥-axis).GO symbols are shown on the other side of the column.

As for QDB group, miR-17-5p, miR-130a, miR-320a, miR-320b, and miR-320c displayed the important role in thenetwork (Figure 3(d)).

4. Discussion

In the long history of traditional clinical practice inChina andother Eastern countries, TCM practitioners have typicallyclassified patients of the same disease into subgroups asdifferent syndromes from a holistic perspective on patients’overall status [10]. Qi-blood theory, a complicated and intri-cate theory, is one of the basic theories of TCM. Qi is usedto describe the refined nutritious substances constituting thehuman body and maintaining life activities. The conceptof blood in TCM is also used to describe body’s functionsand is always regarded the same as the blood in westernmedicine in most conditions, with a clearer definition thanQi [11]. Qi-blood theory is widely used to in the diagnosisand treatment for the chronic diseases such as CHD [12].

However, Qi-stagnation (QSB) and Qi-deficiency (QDB)symptoms in CHD patients are subjective and difficult toevaluate objectively. We hypothesized that microRNAs fromQSB and QDB appearances may reflect characteristics of thesyndromes, which is associated with the status of the Qi-blood.

According to the analysis of the differential miRNAsprofiles, significantly upregulated or downregulated miRNAsare overlapped in the QSB and QDB groups, such as miR-451a, miR-3152, andmiR-4487.This indicates that the parts ofmechanism for QSB and QDB groups were similar and maybe the basis of QSB and QDB syndrome. Based on the upreg-ulated or downregulated value in QSB, the most significantupregulated miRNA is miR-7641 which can significantlysuppress CXCL1, a member of the CXC chemokine familythat promotes neovascularization by binding G-proteincoupled receptors and is related to endothelial cells biogenesissuch as angiogenesis [13]. Consequently, the upregulatedmiR-7641 in QSB patients suggests an abnormal endothelial


QSB QDB

6

Upregulated

84

(a)

QSB QDB

Downregulated

0 253

(b)

HDAC2

PPP2CB

PIK3R1

TCF7L1hsa-miR-455-3p

hsa-miR-4793-3p hsa-miR-4507

hsa-miR-7641

hsa-miR-4484

hsa-miR-619-5p

hsa-miR-8075

PPARGC1A

HTR2C

RUNX1T1CREB5

RXRA

E2F3

SMAD6CREB1 ITGA6

APPL1

CDC42

CCNE2

SMAD7

FGFR1

PPARA

DVL3

CLDN19

TRADD

NFASC

NLGN3

ACVRL1

VCL

NCAM2ITGAL

TRAF4

BCL2L1

PLCG1MPZ

CTNND1FGF9

SP1PPP1CBTAOK3ERBB4

CBL

SMC1AFGF11

CPEB1

MLLT4

CACNA1E

PVRL1 PTCH1

SUFU

PDGFRA

PRKCA

FOXO1

PRKAB2

TNFRSF1B

IRS2

ACP1

CHRD

THBS1ZFYVE16

GDF6ACVR2BACVR2ACD86

CDKN1ADAPK1

IGF1

AKT2

IGF1R

FASLG

PDGFB

E2F2

hsa-miR-32-5p

hsa-miR-6735-5p

hsa-miR-1263

hsa-miR-6126

hsa-miR-6732-5p

hsa-miR-4487

hsa-miR-3152-3p

hsa-miR-4529-3p

hsa-let-7c-5p

GADD45B

NRXN1

SMAD2TGFA

(c)

CBL

MYH10

hsa-miR-6850 5p

hsa-miR-7110-5p

PSD

SH3GL1

PLXNA2hsa-miR-4674

TPM3COL1A1

RYR1

MYLK2

MAP2K7CLDN18

PTCH1 DVL3

SLC2A4

VEGFB

PARD6B

hsa-miR-4507

hsa-miR-425-5p

GADD45B

PRICKLE2

MAP3K13

GIT1

PVRL1

AKT2 EPHA8

PLXNB2TRADD

hsa-miR-4487

SUFU

FGD1

ELK4

DPYSL5

IQSEC2

MMP2

FZD3

BMPR2

GRIN1

PDGFRA

hsa-miR-4529-3p

hsa-miR-7641

hsa-miR-1273g-3p

PRKCA

KRAS

SHC4

SP1

STK4WASF2

MAP3K12

CHRM2ARHGEF12

CACNA1E

MAPK1

ENAH

ACVR1

XIAP

CDKN1ACCND2

PRKAG2

CPEB1CLTC

ACAP2

SH2B3

TBL1XR1NTRK2

SMAP2

YWHAZ TSC1

YWHAQ

ITGB3

GNAI1

PRKG1

PAK7

CALM3CACNA1C

UNC5A

ATP2A3SMAP1

ARPC5RAC1

YWHAG

CRKL

SEMA3A

EPN2

VPS37B

ULK1

E2F3CDK6CREB5

RXRA

GNAZhsa-miR-320c

hsa-miR-320a

hsa-miR-320b

FZD5

hsa-miR-3152-3p

hsa-miR-4440

DUSP16

GRB2

PIK3R1

BCR

DAPK2MAP2K4

CAMKK2TGFBR1

hsa-miR-130a-3p

hsa-miR-6511b-5p

hsa-miR-619-5p

hsa-miR-17-5p

TNF

PDGFD

TGFBR2FGF11

RABEP1EPHA4

TGFAPPARG

E2F1

PSD3

MKNK1

ADIPOR2

FOXO1

EPHB4LDLR

IGF1R

STAT3CRK

DNM2

RAB5BERBB3

RAB11FIP4IQSEC1

SMC1AMAP3K11

CDH5

MAP3K8CRHR1

CACNB1ARFGAP2

CAMK2A

CFL1SSH2

DRD2

CTNND1

DUSP5

RARA

ERBB2

BCL2L1

GNAI2 FLNASLIT1

DGKZ

HBEGF

PRKACA

PLCG1

PSD4GL2

SEMA3F

EIF4E

ADRBK1

VASP

SRCTRIP10

VAV2

VANGL2

TLN1

SFN

PTK2B

PPP3R1

ZFYVE16

ZFYVE20

TIAM1 SEMA5A

SH3KBP1

FGF9

CHPMAP3K2

ABI2

SLC8A2HIF1A

VTA1PTEN

GUCY1A3

EREG

ACVR2B

SMAD4

RALBP1

METRUNX1T1

MAPK9

FLT1NTN1

PFN2

PPARARAB11FIP1

RBL2

ERBB4

PPP1CB

HDAC2

ACVR1C CSNK2A2 CTBP2

hsa-miR-23a-3p

hsa-miR-4793-3p

hsa-miR-23b-3p

hsa-miR-3613-3p

NF1

COL4A4RALB

BMPR1B

EEA1

RAB11A

EPS15 EGLN2

HTR2CDAPK1

DNAJC6

FGFR3CXCL12

RAPGEF2

CALM1

PTK2

PIKFYVE

hsa-miR-548a-3p

PFN1

ID1 MAP2K3

FAM125B

CREBBP

EPHA5

INSR

CHUKEPAS1

JAK1MEF2C

ACSL4

GAB1

hsa-miR-8075

PPARGC1AITPKB

ITGAL

RUNX1

NFAT5

RAB22A

hsa-miR-4484

ACACA

SRF

FGF1

ARHGEF4

LDLRAP1PLD2

EFNB1SEMA4F

NTN4

NEDD4L

DUSP3

EHD2VCL

EXOC7TAOK3

IGF1

PLA2G6

RAB11FIP5

TRAF4

RPS6KA2

TRAF1

hsa-miR-4763-3p

hsa-miR-6735-5p

E2F5

ACVRL1

INHBB

ITGA5

ARHGEF7

hsa-miR-4706

hsa-miR-6750-5p

RNF103

MAP3K14

AGAP2CLDN19 INPP5K

AXIN1

RPS6KA3

SMAD5

TAOK2

ITCH

GNAO1

E2F2

(d)

Figure 3: (a) Venn diagram showed overlapping and nonoverlapping upregulated pathways in QSB and QDB groups. (b) Venn diagramshowed overlapping and nonoverlapping downregulated pathways in QSB and QDB groups. (c) The microRNA-gene-network diagramof QSB. (d) The microRNA-gene-network diagram of QDB. Red squares represent upregulated miRNAs and the blue squares representdownregulated miRNAs. Blue rounds represent the target genes of their connected miRNAs. Black lines represent the regulatory relationbetween miRNAs and their target genes.


Table 2: Upregulated pathways in patients with QSB syndrome.

Path ID Path name Count % 𝑃 value FDRhsa05215 Prostate cancer 16 1.394943 2.63𝐸 − 04 0.020708hsa05214 Glioma 12 1.046207 0.00128 0.065618hsa05218 Melanoma 12 1.046207 0.003441 0.128043hsa04514 Cell adhesion molecules (CAMs) 17 1.482127 0.006066 0.175925hsa05222 Small cell lung cancer 11 0.959024 0.030494 0.42138hsa05223 Non-small cell lung cancer 8 0.697472 0.043989 0.510943

Table 3: (a) Upregulated pathways in patients with QDB syndrome. (b) Downregulated pathways in patients with QDB syndrome.

(a)

Path ID Path name Count % 𝑃 value FDRhsa04144 Endocytosis 41 0.245627 5.65𝐸 − 08 9.66𝐸 − 06

hsa04360 Axon guidance 28 0.167745 1.84𝐸 − 05 0.00157hsa05212 Pancreatic cancer 15 0.089863 0.003962 0.126948hsa04010 MAPK signaling pathway 36 0.215672 0.01223 0.259629hsa05220 Chronic myeloid leukemia 14 0.083873 0.014293 0.239301hsa04070 Phosphatidylinositol signaling system 13 0.077882 0.029505 0.400777hsa04810 Regulation of actin cytoskeleton 28 0.167745 0.041864 0.485632hsa04012 ErbB signaling pathway 14 0.083873 0.043572 0.469973

(b)

Path ID Path name Count % 𝑃 value FDRhsa04012 ErbB signaling pathway 18 1.880878 1.15𝐸 − 05 0.001671hsa05214 Glioma 14 1.462905 6.91𝐸 − 05 0.00503hsa04910 Insulin signaling pathway 21 2.194357 1.33𝐸 − 04 0.006429hsa04510 Focal adhesion 27 2.821317 1.39𝐸 − 04 0.005062hsa04730 Long-term depression 13 1.358412 6.98𝐸 − 04 0.016838hsa05220 Chronic myeloid leukemia 13 1.358412 0.001496 0.030734hsa04720 Long-term potentiation 12 1.253918 0.002133 0.038212hsa04722 Neurotrophin signaling pathway 17 1.776385 0.002769 0.039679hsa04150 mTOR signaling pathway 10 1.044932 0.003329 0.043298hsa05212 Pancreatic cancer 12 1.253918 0.003394 0.04052

cells function which possibly results for the inhibition ofangiogenesis. The most significant downregulated miRNAin QSB group is miR-4484 which differentially expressedbetween macrophages infected with virulent and avirulentmycobacterium tuberculosis [14]. In addition to endothelialcells function, the macrophage dysfunction may be anotherbiological basic of QSB syndrome. According to the log

2fold

change value inQDB group, themost obvious downregulatedmiRNA is miR-320c. It was evidenced that downregulationof miR-320 induced the overexpression of proinflammatorycytokines through the modulation of ERK/NF-𝜅B pathways[15]. Therefore, in the process of QDB, downregulatedmiR-320 may contribute to the proinflammatory cytokinesgeneration.

Based on the results of GO analysis, the functions ofmost differentially regulated genes in QSB were related tothe regulation of cellular component organization, proteinimport into nucleus, glucose metabolic process, and so forth,which indicated that the mechanism for QSB syndromemight relate to the dysfunction of the above biologicalprocess. As for the QDB, the functions of most differentiallyregulated genes were related to protein kinase cascade,

regulation of small GTPase mediated signal transduction,vesicle-mediated transport, and so forth, suggesting thatQSB syndrome might relate to the dysfunction of the abovebiological process.

According to the KEGG analysis, 6 pathways were relatedto the QSB, 5 of which were related to the cancer concernedpathways.The only pathway that did not belong to the cancerconcerned pathway was cell adhesion molecules (CAMs).Cell-cell and cell-matrix interactions are mediated throughseveral different families of CAMs including the selectins,the integrins, the cadherins, and the immunoglobulins.CAMs play a very significant and critical role in both heartdevelopment and different pathophysiological heart disease[16]. Blood stasis syndrome has a certain degree of highcoagulation state. On this pathological basis, cell adhesionmolecules weremore significant in the QSB than that in QDBwhich indicated that CAMs playmore important role in QSB.We assumed that severalmembers in theCAMs superfamiliesand in particular the integrin family will serve as diagnosticstrategies with QSB. The number of pathway with obviouschange in QDB group is more than QSB group, parts ofwhich are associated with phagocytosis such as endocytosis,


mTOR signaling pathway which is involved in the process ofautophagy. All types of cells in the body use the endocytosisprocess to communicate with the biological environments.This process is an energy-dependent process through whichcells internalize ions and biomolecules [17]. In addition, it isevidenced that the cellular rate of nutrient or ion uptake (e.g.,glucose, Fe3+, and K+) or efflux (e.g., Na+) is governed bya complement of membrane transporters and receptors thatshow dynamic localization at both the plasmamembrane andthe defined intracellularmembrane compartments [18]. Someresearch showed that endocytosis was upregulated in borderzone cardiomyocytes, and inhibition of endocytosis mightbe an effective approach to prevent export of injury signalsfrom the myocardial infarct size [19]. In considering endo-cytosis as the most obvious upregulated pathway in QDB,we speculate that endocytosis process consumes excessivethe above mentioned nutrients so that the body appears aseries of energy deficiency signs, as so-called Qi-deficiency.mTOR signaling pathway is one of regulatory molecules thatnegatively controls autophagy [20–22]. Consequently, thedownregulatedmTOR signaling pathway inQDB can activateautophagy which is evidenced to be harmful for myocardialischemia reperfusion [23, 24]. The downregulated mTORsignaling pathway in CHD patients with QDB syndromeresults in the activation of autophagy which could increasemyocardial cell apoptosis. Therefore, the autophagy may beanother biological characteristic of QDB syndrome differentfrom QSB syndrome.

The construction of microRNA-gene-network in QSBgroup demonstrates that let-7c, miR-4487, miR-619, miR-8075, miR-6735, and miR-32-5p are located in the centerof diagram, suggesting that the abnormal miRNAs mayhave the most important and extensive regulatory functionfor Qi-stagnation. The construction of microRNA-gene-network in QDB displays that miR-17-5p, miR-130a, miR-320a, miR-320b, and miR-320c are located in the center ofdiagram, suggesting that the abnormal miRNAs may havethe most important and extensive regulatory function for Qi-deficiency. Further, we found that though miR-4487, miR-619, and miR-8075 were the overlapping miRNAs in bothQSB and QDB syndromes, their regulatory function seemedmore wide and involved in more regulatory networks inQSB syndromes than that in QDB syndromes. Of note,miR-320 family including miR-320a, miR-320b, and miR-320c regulates QDB syndromes predominantly. MiR-320has been found to have widespread biological effects as itregulates multiple important molecules. Its potential targetsinclude ET-1, ERK1, VEGF, and FN (http://www.microrna.org/). The biologic actions of miR-320 involve carcinogen-esis, development, and ischemia reperfusion injury [25, 26].CombinedwithQi-deficiency characteristic, we consider thatmiR-320 family plays a crucial role in various performanceswith insufficient energy and nutrient.

In this paper, we compared molecular networks of twosyndromes of CDH patients with blood stasis and analyzedtheir common and different microRNAs mechanisms. Weachieve a better understanding of the biological characteris-tics of syndrome differentiation of CHD which is beneficial

to explore different therapies to enhance the effectiveness andpertinence of treatment.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Authors’ Contribution

Dr. Jincai Hou and Dr. Jun Wang contribute equally to thiswork and are co-first authors.

Acknowledgments

This research was supported by the National Basic ResearchProgram of China (973 Program, 2015CB554400); theNational Science & Technology Major Project of China(2012ZX09301002-004-002 and 2012ZX09103201-049); theNational Natural Science Foundation of China (81473449and 81102679); and the Fundamental Research Funds for theCentral public welfare research institutes (ZZ070824).

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